Multimodal knowledge sharing networks a research agenda
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Multimodal Knowledge Sharing Networks: A Research Agenda. Maryam Alavi Goizueta Business School Emory University. Agenda *. Background Perspectives on knowledge sharing in organizations The proposed multimodal perspective on knowledge sharing

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Multimodal knowledge sharing networks a research agenda

Multimodal Knowledge Sharing Networks: A Research Agenda

Maryam Alavi

Goizueta Business School

Emory University


Agenda

Agenda*

  • Background

  • Perspectives on knowledge sharing in organizations

  • The proposed multimodal perspective on knowledge sharing

  • A field study of multimodal knowledge sharing networks

  • Findings and future direction for research

    * With Gerald Kane, Boston College


Study of knowledge sharing ks in organizations

Study of Knowledge Sharing (KS) in Organizations

  • Existing KS research adopts two distinct perspectives: Social and technological

    • Social perspective focuses on nature and structure of interpersonal relationships for KS. Uses social networks as a framework for study of relations

    • Technological perspective focuses on information systems and tools that can be leveraged for KS. Capture and sharing codified knowledge


The challenge

The Challenge

  • Both perspectives are indispensable for understanding KS performance

    • Can have a technological infrastructure in place, but if people not inclined to use it, then ineffective.

    • Can have strong social knowledge sharing in place, but if given inadequate tools, then also ineffective.


Multimodal knowledge sharing networks a research agenda

  • Key aspects of knowledge sharing in large and complex organizations may be overlooked if either the social or the user-system interactions are examined independently

  • Multimodality: Fusing digital and social networks


Multimodal knowledge networks

Individuals

IS

Multimodal Knowledge Networks

  • Combining the social network perspective with the technological perspective on knowledge sharing


Knowledge sharing in multimodal networks

Knowledge Sharing in Multimodal Networks

  • Knowledge is shared through Interpersonal exchanges among individuals as well as direct use of IS nodes comprising the network


Social network perspective

Social Network Perspective

  • Conceptualizes individuals as “nodes” and the relationships among them as “ties”

    • Strength of “ties” and location of nodes impact knowledge sharing in social networks

    • Tie strength: Frequency and depth of interactions between two nodes


Research on relationship between tie strength and ks

Research on Relationship Between Tie Strength and KS


Multimodal knowledge sharing networks a research agenda

  • In a multimodal network, as multiple individuals come together to share knowledge, the overall tie strength among individuals enhances knowledge sharing.

  • Overall tie strengths in a network is referred to as “density”


Multimodal knowledge sharing networks a research agenda

  • Density

    • The ratio of actual ties to the number of possible ties in a network (Brass, 1995)


Hypothesis 1

Hypothesis 1

  • The density of the interpersonal connections in a multimodal knowledge sharing network is positively related to outcomes


Knowledge sharing in multimodal networks1

Knowledge Sharing in Multimodal Networks

  • Knowledge is shared through interpersonal interactions among individuals as well as direct use of ISnodes comprising the network


Technological perspective on ks

Technological Perspective on KS

  • IS tools enable individuals to store, search and retrieve vast amounts and types of knowledge

  • Whether and how individuals interact and use the systems impact knowledge sharing


Is use

IS Use

  • Frequency of user interactions with information system (e.g., Devaraj et al. 2003)

  • Depth (features and functionality) of user-system interactions ( Griffith 1999, Jasperson et al. 2005)


Multimodal knowledge sharing networks a research agenda

  • How multiple systems are used together (Vertegaal 2003 & Sambamurthy et al. 2003)

  • Need to consider the frequency and depth of use between users and all systems together to study multimodal network performance

  • An example


Hypothesis 2

Hypothesis 2

  • Average tie strength (frequency and depth) between users and systems in a multimodal network is positively related to outcomes


Multimodal knowledge sharing networks a research agenda

Individuals

IS

  • Social network perspective on knowledge sharing: H1


Multimodal knowledge sharing networks a research agenda

Individuals

IS

  • User-system (IS) perspective on knowledge sharing: H2


Multimodality fusing social and digital networks

Individuals

IS

Multimodality: Fusing Social and Digital Networks

  • Combining the social network perspective with the IS perspective on knowledge sharing


Is nodes centrality

Individuals

IS

IS Nodes Centrality

  • A function of user-system relationship, and of user with other network members


Hypothesis 3

Hypothesis 3

  • The centrality of IS in a multimodal network is positively related to knowledge sharing outcomes


Research setting and method

Research Setting and Method

  • Conducted in a regional division of a national health maintenance organization in the U.S.

  • Studied multimodal networks consisting of healthcare provider teams and the IS at their disposals

  • Each team: 4-6 physicians and 8-10 clinical and administrative support

  • 40 teams participated in the study


Research setting and method1

Research Setting and Method

  • Each team had a risk adjusted panel of about 2000 patients

  • The teams were independent of each other (they were at different physical locations)

  • They had similar tasks (providing primary care)

  • Each team had access to the same six IS provided by the company.


Recall the hypotheses

Recall the Hypotheses

  • H1: The density of the interpersonal connections in a multimodal knowledge sharing network is positively related to outcomes

  • H2: Average tie strength (frequency and depth) between users and systems in a multimodal network is positively related to outcomes

  • H3: The centrality of IS in a multimodal network is positively related to KS outcomes


Research variables

Research Variables

  • Independent variables

    • Interpersonal network density (the average frequency and depth of interactions among team members)

    • User-system tie strength (frequency and depth of interactions between user and system aggregated across all systems)

    • IS centrality (eigenvector centrality of each system averaged across all systems within the team)

      • Eigenvector was calculated using UCINet 6.97 (Borgatti et al. 2002)


Sample survey questions to assess multimodal network structure

Sample Survey Questions to Assess Multimodal Network Structure

  • How frequently do you interact with this person?

    1 – Never 2 – Rarely 3 – A few times per month 4 – Weekly 5 – Daily 6 – A few times a day 7 – Hourly or more

  • How frequently do you interact with this system (i.e., personally use with keyboard and/or mouse)?

    1 – Never 2 – Rarely 3 – A few times per month 4 – Weekly 5 – Daily 6 – A few times a day 7 – Hourly or more


Research variables1

Research Variables

  • Dependent variables

    • Efficiency of care: the time it takes a patient to see a doctor after he signs in at the office

    • Quality of care: if a team’s patients are getting the required tests and treatments recognized as “best practices” within the industry (e.g.,% breast cancer screening)

    • Chronic care outcomes: whether a patient’s chronic disease is under control according to test results


Research variables2

Research Variables

Control variables

  • Physician level: age, tenure, gender, position (team leader)

  • Patient level (for chronic care outcome): eye exam, cholesterol screening, nephropathy screening, insurance plan

  • Team level: average age, diversity, average tenure, composition


Results

Results

  • Efficiency of care:

    • The density of the interpersonal exchanges is negatively related to patient wait time (t = -2.938, p<.01), H1 supported

    • The average tie strength between systems and users is also negatively related to patient wait time (t = - 2.909, p<.01), H2 supported

    • The centrality of the IS within the team is also negatively related to patient wait time (t = -2.174, p<.01), H3 supported


Results1

Results

  • Quality of care:

    • Density of interpersonal interactions is not significant (H1 is not supported)

    • Average tie strength is significant, but in the opposite direction! (contradicts H2)

    • Centrality of IS is significant in terms of quality of care (H3 supported)


Results2

Results

  • Chronic care outcomes:

    • Density of interpersonal interactions is not significant , H1 is not supported

    • Average tie strength with systems is significant, but in the opposite direction! H2 contradicted

    • Centrality of IS is significant (z = 2.53, p<.01), H3 is supported


Results discussion

Results & Discussion


Limitations

Limitations

  • Low generalizablity, beyond healthcare delivery teams

  • A snapshot of a network structure, versus evolution of networks over time


Future research

Future Research

  • Study of performance implications of how centrality of individuals in different roles can impact performance (e.g., physicians)

  • Study of cognitive and relational dimensions of multimodal networks on performance

  • Developing the theoretical mechanisms that drive multimodal network formations and performance


Summary

Summary

  • How to incorporate both social and technological perspectives into a single framework

  • Multimodal knowledge networks address the interrelationship between humans and IS in organization

  • Drawing upon social network analysis (SNA) from management/sociology literature to explore structural features leading to knowledge sharing


Multimodal knowledge sharing networks a research agenda

The End


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